Automated methods to generate product recommendations for a product type such as cars and trucks are typically non-user friendly, ineffective, and uncomfortable to use. Various approaches to generating recommendations have been proposed, though each suffers from various deficiencies.
Filtering systems, for example, ask for allowable ranges of product attributes and then show the list of products that have all their attributes within the ranges specified. In many cases, the system recommends either too many or too few (often zero) product recommendations. Products within the limits of the ranges are not listed in order of desirability. The systems exhibit no intrinsic knowledge of consumer values and as a result, obvious connections between attributes are ignored and effective recommendations based on a user's preferences are not given.
In rule-based systems, elaborate and complex rules are set up for translating user statements about desirable features of a product into a list of recommended products for the consumer. These systems are referred to by such names such as fuzzy logic, artificial intelligence, expert systems and neural nets. In some cases, scoring systems are used. In other cases, experts are asked to define the rules. Since these systems do not try to model the underlying values of consumers for product attributes, the data is not very useful for helping manufacturers design new products. The lack of a systematic logic for connecting user data to product recommendations requires that a new system must be constructed for each new product area, which makes them expensive to construct.
Conjoint measurement systems commonly ask a series of trade-off questions, typically eight to twelve of them, along with a few other types of questions. Standard conjoint measurement algorithms are used to calculate estimates of user values and then the estimated values are used to calculate recommendations. Many users dislike tradeoff questions and when they do, they are likely to abandon the interview. The interview of conjoint measurements systems may be excessively long, and typically cannot be interrupted to get intermediate recommendations. Users cannot go back and change answers and check the effect of those changes on the recommendations.
On-line interviews, like conjoint measurement systems, can use trade-off questions to find out what product features customers want. Many people are uncomfortable with and even antagonistic to this interview approach of tradeoff questions, and any interview that makes an interviewee uncomfortable, will probably be unsuccessful. Instead, the questions and recommendation methodology of any interview process should be well aligned with an intuitive notion of what a good recommendation should be. A better interview approach poses more user-friendly questions, for example, “What is important to you when you buy a vehicle?”, with easy-to-use, interactive answer selection styles using selectors such as radio buttons, checkboxes, sliders, icons, dialog boxes, numerical input and other graphical user interface techniques.
One objective of this invention is to provide a web-based application that generates product recommendations based on answers to questions and user-selected preferences from which inferences and recommendations can be made. Consumers who use the website should be able to answer a few questions about their preferences for a product, after which the website provides them a list of recommended products, ranked in order of the estimated value of the product to the user. The questions should be understandable and easy to answer. Moreover, users should be allowed to decide which questions to answer and when they want to see the list of recommended products. Users should be able to go back and change answers to previously answered questions or to answer additional questions in a continuing dialog with the website. The data from the users may be stored and used to estimate the aggregate response of classes of users in a wide variety of future market scenarios.
Another objective of this invention is to provide consumers with highquality and unbiased recommendations about the products of a particular market, which are consistent with their preferences for specific product attributes. Another objective of the invention is to collect consumer preference data using the recommendation engine. The collected preference data can guide product manufacturers in design and improvements of their products, making them more responsive to the diversity of consumer needs and preferences.
Consumers should have a more flexible and user-friendly interface for recording their product preferences, an interface that uses questions that are easy to understand, avoids rigid interview schedules, allows modifications to earlier answers or answers to new questions, and allows the user to observe how changed or new answers affect the set of recommended products. Another objective of this invention is to offer consumers an easier way to find products that best meet individual consumer needs and preferences for products, thereby increasing their satisfaction and the efficiency of the free market.
It is an object of this invention, therefore, to provide a method and a system to generate a product recommendation for a product type, and to overcome the challenges and deficiencies described above.